---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 스타스포츠 스타 루카스 스포츠용품 운동신발 족구화 스포츠/레저>족구>족구화
- text: 스텝 레더 축구 연습 훈련 사다리 순발력 족구 연습기 스포츠/레저>족구>기타족구용품
- text: 낫소 족구공 큐스팩트A T패널 적용 EVA FORM 쿠션감 스포츠/레저>족구>족구공
- text: 더블레이어 고탄력 터치감 족구공 족구동호회 족구볼 스포츠/레저>족구>족구공
- text: 라인기 운동장 경기장 선긋기 트랙 축구장 족구장 주차장 스포츠/레저>족구>기타족구용품
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
model-index:
- name: SetFit with mini1013/master_domain
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with mini1013/master_domain

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label | Examples                                                                                                                                                                                                       |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0   | <ul><li>'족구화 발볼넓은 JOMA 호머 축구 신발 남성 MG 짧은 손톱 조마 학생 성인 프리스비 스포츠/레저>족구>족구화'</li><li>'신신상사 스타스포츠 스타 에너제틱 족구화 선수용 JS6200-03 245 스포츠/레저>족구>족구화'</li><li>'스타스포츠 스타 레독스R 족구화 입문자 동호회용 JS5970 스포츠/레저>족구>족구화'</li></ul>  |
| 1.0   | <ul><li>'신신상사 스타스포츠 스타스포츠 족구공 사인볼 하이브리드 8판넬방식 스포츠/레저>족구>족구공'</li><li>'신신상사 스타스포츠 족구공 풋살공 축구공 족구공 더 윙 태극 8판넬 JB435 스포츠/레저>족구>족구공'</li><li>'스타스포츠 스타스포츠 태극 족구공 동계용 방수코팅 족구 시합구 스포츠/레저>족구>족구공'</li></ul>          |
| 0.0   | <ul><li>'족구타격기 발차기 훈련 스탠드 연습 운동 레슨 스포츠/레저>족구>기타족구용품'</li><li>'스타 족구 심판대 의자 심판 발판대 라인기 코트경계망 스코어보드 지주세트 네트 번호판 스포츠/레저>족구>기타족구용품'</li><li>'ZIPPO 라이터 Figurehead GD 신명글로빅스 ZPM3MA007R 스포츠/레저>족구>기타족구용품'</li></ul> |
| 3.0   | <ul><li>'브럼비 족구유니폼 사이트 21시즌 디자인 피오드 - 2 스포츠/레저>족구>족구의류'</li><li>'브럼비 축구유니폼 사이트 24시즌 디자인 로마-2 스포츠/레저>족구>족구의류'</li><li>'족구 유니폼제작 전사팀복 221007 스포츠/레저>족구>족구의류'</li></ul>                                           |
| 2.0   | <ul><li>'낫소 일반형 족구네트 NSJ-N105 스포츠/레저>족구>족구네트'</li><li>'오레인 족구 네트 OJG-N224 스포츠/레저>족구>족구네트'</li><li>'엔포유 N4U-B500 배드민턴 족구 다용도네트 스포츠/레저>족구>족구네트'</li></ul>                                                        |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_sl26")
# Run inference
preds = model("스타스포츠 스타 루카스 스포츠용품 운동신발 족구화 스포츠/레저>족구>족구화")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 8.0441 | 19  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 15                    |
| 3.0   | 70                    |
| 4.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0172  | 1    | 0.4882        | -               |
| 0.8621  | 50   | 0.4668        | -               |
| 1.7241  | 100  | 0.1034        | -               |
| 2.5862  | 150  | 0.0002        | -               |
| 3.4483  | 200  | 0.0           | -               |
| 4.3103  | 250  | 0.0           | -               |
| 5.1724  | 300  | 0.0           | -               |
| 6.0345  | 350  | 0.0           | -               |
| 6.8966  | 400  | 0.0           | -               |
| 7.7586  | 450  | 0.0           | -               |
| 8.6207  | 500  | 0.0           | -               |
| 9.4828  | 550  | 0.0           | -               |
| 10.3448 | 600  | 0.0           | -               |
| 11.2069 | 650  | 0.0           | -               |
| 12.0690 | 700  | 0.0           | -               |
| 12.9310 | 750  | 0.0           | -               |
| 13.7931 | 800  | 0.0           | -               |
| 14.6552 | 850  | 0.0           | -               |
| 15.5172 | 900  | 0.0           | -               |
| 16.3793 | 950  | 0.0           | -               |
| 17.2414 | 1000 | 0.0           | -               |
| 18.1034 | 1050 | 0.0           | -               |
| 18.9655 | 1100 | 0.0           | -               |
| 19.8276 | 1150 | 0.0           | -               |
| 20.6897 | 1200 | 0.0           | -               |
| 21.5517 | 1250 | 0.0           | -               |
| 22.4138 | 1300 | 0.0           | -               |
| 23.2759 | 1350 | 0.0           | -               |
| 24.1379 | 1400 | 0.0           | -               |
| 25.0    | 1450 | 0.0           | -               |
| 25.8621 | 1500 | 0.0           | -               |
| 26.7241 | 1550 | 0.0           | -               |
| 27.5862 | 1600 | 0.0           | -               |
| 28.4483 | 1650 | 0.0           | -               |
| 29.3103 | 1700 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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